##############################################################
# Analisis de Componentes Principales del dataset de Ozono #
# #
# Creado: 2020-09-06 v. 2020-09-10 #
# Ultima Mod: plots #
# #
# Grupo B: GAD, Benitez, Garcia, Rechimon, Rodriguez #
# #
##############################################################
##### IMPORTAMOS LAS BIBLIOTECAS A USAR
library(readxl)
library(corrplot)
## corrplot 0.84 loaded
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
library(psych)
library(rela)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
##
## first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
options(scipen = 6)
###### LEEMOS EL DATASET
data <- read_excel("ozono.xls", na = "?")
# Corroboramos que se haya leido bien
head(data, 5)
## # A tibble: 5 x 74
## Date WSR0 WSR1 WSR2 WSR3 WSR4 WSR5 WSR6 WSR7 WSR8
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1998-01-01 00:00:00 0.8 1.8 2.4 2.1 2 2.1 1.5 1.7 1.9
## 2 1998-01-02 00:00:00 2.8 3.2 3.3 2.7 3.3 3.2 2.9 2.8 3.1
## 3 1998-01-03 00:00:00 2.9 2.8 2.6 2.1 2.2 2.5 2.5 2.7 2.2
## 4 1998-01-04 00:00:00 4.7 3.8 3.7 3.8 2.9 3.1 2.8 2.5 2.4
## 5 1998-01-05 00:00:00 2.6 2.1 1.6 1.4 0.9 1.5 1.2 1.4 1.3
## # ... with 64 more variables: WSR9 <dbl>, WSR10 <dbl>, WSR11 <dbl>,
## # WSR12 <dbl>, WSR13 <dbl>, WSR14 <dbl>, WSR15 <dbl>, WSR16 <dbl>,
## # WSR17 <dbl>, WSR18 <dbl>, WSR19 <dbl>, WSR20 <dbl>, WSR21 <dbl>,
## # WSR22 <dbl>, WSR23 <dbl>, WSR_PK <dbl>, WSR_AV <dbl>, T0 <dbl>, T1 <dbl>,
## # T2 <dbl>, T3 <dbl>, T4 <dbl>, T5 <dbl>, T6 <dbl>, T7 <dbl>, T8 <dbl>,
## # T9 <dbl>, T10 <dbl>, T11 <dbl>, T12 <dbl>, T13 <dbl>, T14 <dbl>, T15 <dbl>,
## # T16 <dbl>, T17 <dbl>, T18 <dbl>, T19 <dbl>, T20 <dbl>, T21 <dbl>,
## # T22 <dbl>, T23 <dbl>, T_PK <dbl>, T_AV <dbl>, T85 <dbl>, RH85 <dbl>,
## # U85 <dbl>, V85 <dbl>, HT85 <dbl>, T70 <dbl>, RH70 <dbl>, U70 <dbl>,
## # V70 <dbl>, HT70 <dbl>, T50 <dbl>, RH50 <dbl>, U50 <dbl>, V50 <dbl>,
## # HT50 <dbl>, KI <dbl>, TT <dbl>, SLP <dbl>, SLP_ <dbl>, Precp <dbl>,
## # clase <dbl>
# Visualizamos el numero de variables y de casos
ncol(data)
## [1] 74
nrow(data)
## [1] 2534
##### PREPARACION DE LOS DATOS
# Visualizamos el numero de casos con null
sapply(data, function(x) sum(is.na(x)))
## Date WSR0 WSR1 WSR2 WSR3 WSR4 WSR5 WSR6 WSR7 WSR8 WSR9
## 0 299 292 294 292 293 292 291 289 290 287
## WSR10 WSR11 WSR12 WSR13 WSR14 WSR15 WSR16 WSR17 WSR18 WSR19 WSR20
## 288 292 287 288 288 286 284 283 286 292 294
## WSR21 WSR22 WSR23 WSR_PK WSR_AV T0 T1 T2 T3 T4 T5
## 293 300 297 273 273 190 185 187 184 184 183
## T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16
## 183 183 185 185 188 192 189 191 192 187 184
## T17 T18 T19 T20 T21 T22 T23 T_PK T_AV T85 RH85
## 182 184 188 189 185 192 189 175 175 99 105
## U85 V85 HT85 T70 RH70 U70 V70 HT70 T50 RH50 U50
## 180 180 95 107 115 157 157 100 115 125 210
## V50 HT50 KI TT SLP SLP_ Precp clase
## 210 112 136 125 95 158 2 0
data <- na.omit(data)
sapply(data, function(x) sum(is.na(x)))
## Date WSR0 WSR1 WSR2 WSR3 WSR4 WSR5 WSR6 WSR7 WSR8 WSR9
## 0 0 0 0 0 0 0 0 0 0 0
## WSR10 WSR11 WSR12 WSR13 WSR14 WSR15 WSR16 WSR17 WSR18 WSR19 WSR20
## 0 0 0 0 0 0 0 0 0 0 0
## WSR21 WSR22 WSR23 WSR_PK WSR_AV T0 T1 T2 T3 T4 T5
## 0 0 0 0 0 0 0 0 0 0 0
## T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16
## 0 0 0 0 0 0 0 0 0 0 0
## T17 T18 T19 T20 T21 T22 T23 T_PK T_AV T85 RH85
## 0 0 0 0 0 0 0 0 0 0 0
## U85 V85 HT85 T70 RH70 U70 V70 HT70 T50 RH50 U50
## 0 0 0 0 0 0 0 0 0 0 0
## V50 HT50 KI TT SLP SLP_ Precp clase
## 0 0 0 0 0 0 0 0
nrow(data)
## [1] 1847
# Hacemos un resumen de los datos
summary(data)
## Date WSR0 WSR1 WSR2
## Min. :1998-01-01 00:00:00 Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:1999-10-14 12:00:00 1st Qu.:0.600 1st Qu.:0.60 1st Qu.:0.600
## Median :2001-04-02 00:00:00 Median :1.300 Median :1.30 Median :1.200
## Mean :2001-06-04 17:53:34 Mean :1.629 Mean :1.57 Mean :1.537
## 3rd Qu.:2003-06-06 12:00:00 3rd Qu.:2.400 3rd Qu.:2.30 3rd Qu.:2.200
## Max. :2004-12-31 00:00:00 Max. :6.900 Max. :6.90 Max. :7.100
## WSR3 WSR4 WSR5 WSR6 WSR7
## Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.60 1st Qu.:0.600 1st Qu.:0.700 1st Qu.:0.800 1st Qu.:1.200
## Median :1.30 Median :1.300 Median :1.300 Median :1.400 Median :1.900
## Mean :1.52 Mean :1.515 Mean :1.534 Mean :1.632 Mean :2.038
## 3rd Qu.:2.20 3rd Qu.:2.200 3rd Qu.:2.100 3rd Qu.:2.200 3rd Qu.:2.800
## Max. :6.70 Max. :7.200 Max. :7.400 Max. :7.200 Max. :7.500
## WSR8 WSR9 WSR10 WSR11 WSR12
## Min. :0.100 Min. :0.100 Min. :0.100 Min. :0.10 Min. :0.100
## 1st Qu.:1.700 1st Qu.:2.000 1st Qu.:2.100 1st Qu.:2.00 1st Qu.:2.000
## Median :2.400 Median :2.800 Median :2.900 Median :3.00 Median :3.000
## Mean :2.526 Mean :2.843 Mean :2.982 Mean :3.03 Mean :3.057
## 3rd Qu.:3.300 3rd Qu.:3.600 3rd Qu.:3.800 3rd Qu.:3.90 3rd Qu.:4.000
## Max. :9.200 Max. :8.300 Max. :8.700 Max. :8.80 Max. :9.000
## WSR13 WSR14 WSR15 WSR16
## Min. :0.100 Min. :0.100 Min. :0.000 Min. :0.200
## 1st Qu.:2.000 1st Qu.:2.100 1st Qu.:2.200 1st Qu.:2.200
## Median :3.000 Median :3.100 Median :3.200 Median :3.200
## Mean :3.114 Mean :3.185 Mean :3.235 Mean :3.202
## 3rd Qu.:4.100 3rd Qu.:4.200 3rd Qu.:4.200 3rd Qu.:4.150
## Max. :9.600 Max. :9.100 Max. :8.900 Max. :8.700
## WSR17 WSR18 WSR19 WSR20
## Min. :0.200 Min. :0.100 Min. :0.000 Min. :0.000
## 1st Qu.:2.100 1st Qu.:1.700 1st Qu.:1.400 1st Qu.:1.300
## Median :2.900 Median :2.500 Median :2.200 Median :2.000
## Mean :2.944 Mean :2.582 Mean :2.301 Mean :2.105
## 3rd Qu.:3.800 3rd Qu.:3.400 3rd Qu.:3.100 3rd Qu.:2.900
## Max. :8.100 Max. :7.500 Max. :7.100 Max. :8.700
## WSR21 WSR22 WSR23 WSR_PK
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :1.200
## 1st Qu.:1.100 1st Qu.:0.900 1st Qu.:0.700 1st Qu.:3.400
## Median :1.700 Median :1.600 Median :1.400 Median :4.100
## Mean :1.953 Mean :1.814 Mean :1.718 Mean :4.176
## 3rd Qu.:2.700 3rd Qu.:2.500 3rd Qu.:2.500 3rd Qu.:4.900
## Max. :9.300 Max. :7.700 Max. :8.300 Max. :9.600
## WSR_AV T0 T1 T2
## Min. :0.500 Min. :-1.80 Min. :-2.10 Min. :-2.60
## 1st Qu.:1.600 1st Qu.:14.10 1st Qu.:13.80 1st Qu.:13.35
## Median :2.200 Median :20.70 Median :20.40 Median :20.20
## Mean :2.316 Mean :18.98 Mean :18.66 Mean :18.38
## 3rd Qu.:2.900 3rd Qu.:24.75 3rd Qu.:24.50 3rd Qu.:24.25
## Max. :6.400 Max. :29.90 Max. :29.00 Max. :28.80
## T3 T4 T5 T6
## Min. :-2.10 Min. :-2.70 Min. :-2.30 Min. :-2.20
## 1st Qu.:13.20 1st Qu.:12.80 1st Qu.:12.60 1st Qu.:12.50
## Median :20.20 Median :19.90 Median :19.90 Median :20.00
## Mean :18.14 Mean :17.92 Mean :17.79 Mean :17.92
## 3rd Qu.:24.10 3rd Qu.:23.90 3rd Qu.:23.90 3rd Qu.:24.20
## Max. :28.30 Max. :28.10 Max. :28.20 Max. :28.70
## T7 T8 T9 T10
## Min. :-2.30 Min. :-1.90 Min. :-1.20 Min. :-1.20
## 1st Qu.:13.20 1st Qu.:14.70 1st Qu.:16.50 1st Qu.:17.90
## Median :20.70 Median :21.80 Median :23.20 Median :24.30
## Mean :18.77 Mean :20.15 Mean :21.59 Mean :22.82
## 3rd Qu.:25.40 3rd Qu.:26.75 3rd Qu.:28.10 3rd Qu.:29.10
## Max. :30.10 Max. :31.40 Max. :33.80 Max. :36.40
## T11 T12 T13 T14
## Min. : 0.20 Min. : 0.30 Min. : 0.90 Min. : 1.50
## 1st Qu.:18.90 1st Qu.:19.80 1st Qu.:20.30 1st Qu.:20.80
## Median :25.10 Median :25.60 Median :25.80 Median :25.90
## Mean :23.74 Mean :24.37 Mean :24.78 Mean :25.04
## 3rd Qu.:29.80 3rd Qu.:30.20 3rd Qu.:30.30 3rd Qu.:30.40
## Max. :38.50 Max. :40.40 Max. :41.30 Max. :41.60
## T15 T16 T17 T18 T19
## Min. : 1.70 Min. : 0.60 Min. :-0.60 Min. :-0.2 Min. : 0.10
## 1st Qu.:20.90 1st Qu.:20.40 1st Qu.:19.75 1st Qu.:18.6 1st Qu.:17.55
## Median :25.80 Median :25.40 Median :24.70 Median :23.8 Median :22.80
## Mean :25.04 Mean :24.71 Mean :23.94 Mean :22.8 Mean :21.73
## 3rd Qu.:30.30 3rd Qu.:30.00 3rd Qu.:29.20 3rd Qu.:28.1 3rd Qu.:27.05
## Max. :41.30 Max. :41.10 Max. :39.90 Max. :37.8 Max. :36.10
## T20 T21 T22 T23 T_PK
## Min. : 0.20 Min. : 0.40 Min. :-0.10 Min. :-0.4 Min. : 1.70
## 1st Qu.:16.60 1st Qu.:15.70 1st Qu.:15.00 1st Qu.:14.6 1st Qu.:21.70
## Median :22.20 Median :21.70 Median :21.30 Median :21.1 Median :26.70
## Mean :20.94 Mean :20.35 Mean :19.85 Mean :19.4 Mean :25.89
## 3rd Qu.:26.30 3rd Qu.:25.80 3rd Qu.:25.40 3rd Qu.:25.1 3rd Qu.:31.10
## Max. :34.60 Max. :33.40 Max. :32.60 Max. :31.3 Max. :41.60
## T_AV T85 RH85 U85
## Min. : 0.30 Min. :-4.50 Min. :0.0100 Min. :-15.770
## 1st Qu.:16.50 1st Qu.:10.80 1st Qu.:0.3900 1st Qu.: -1.070
## Median :22.50 Median :14.40 Median :0.6300 Median : 1.750
## Mean :21.16 Mean :13.71 Mean :0.5764 Mean : 1.976
## 3rd Qu.:26.80 3rd Qu.:17.40 3rd Qu.:0.7900 3rd Qu.: 4.740
## Max. :33.60 Max. :24.50 Max. :1.0000 Max. : 18.320
## V85 HT85 T70 RH70
## Min. :-16.130 Min. :1357 Min. :-9.900 Min. :0.0100
## 1st Qu.: -2.170 1st Qu.:1514 1st Qu.: 3.700 1st Qu.:0.1700
## Median : 1.720 Median :1537 Median : 6.800 Median :0.3700
## Mean : 1.941 Mean :1534 Mean : 6.074 Mean :0.3989
## 3rd Qu.: 6.080 3rd Qu.:1558 3rd Qu.: 9.000 3rd Qu.:0.6100
## Max. : 22.160 Max. :1642 Max. :16.200 Max. :1.0000
## U70 V70 HT70 T50
## Min. :-14.370 Min. :-23.680 Min. :2919 Min. :-24.8
## 1st Qu.: 0.590 1st Qu.: -2.775 1st Qu.:3121 1st Qu.:-13.2
## Median : 4.770 Median : 0.880 Median :3156 Median :-10.1
## Mean : 5.165 Mean : 1.010 Mean :3148 Mean :-10.5
## 3rd Qu.: 9.790 3rd Qu.: 4.720 3rd Qu.:3182 3rd Qu.: -7.4
## Max. : 28.210 Max. : 25.540 Max. :3249 Max. : -1.7
## RH50 U50 V50 HT50
## Min. :0.0100 Min. :-14.920 Min. :-25.9900 Min. :5480
## 1st Qu.:0.0900 1st Qu.: 2.705 1st Qu.: -3.9900 1st Qu.:5775
## Median :0.2200 Median : 9.220 Median : 0.2600 Median :5835
## Mean :0.3001 Mean : 9.821 Mean : 0.6472 Mean :5822
## 3rd Qu.:0.4700 3rd Qu.: 16.505 3rd Qu.: 4.6450 3rd Qu.:5880
## Max. :1.0000 Max. : 41.360 Max. : 30.4200 Max. :5965
## KI TT SLP SLP_
## Min. :-56.70 Min. :-10.10 Min. : 9995 Min. :-135.0000
## 1st Qu.: -2.75 1st Qu.: 33.05 1st Qu.:10130 1st Qu.: -20.0000
## Median : 14.70 Median : 41.35 Median :10160 Median : 0.0000
## Mean : 10.68 Mean : 37.69 Mean :10165 Mean : -0.8365
## 3rd Qu.: 27.82 3rd Qu.: 45.15 3rd Qu.:10195 3rd Qu.: 15.0000
## Max. : 42.05 Max. : 59.15 Max. :10350 Max. : 140.0000
## Precp clase
## Min. : 0.0000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.:0.0000
## Median : 0.0000 Median :0.0000
## Mean : 0.3588 Mean :0.0693
## 3rd Qu.: 0.0500 3rd Qu.:0.0000
## Max. :20.6500 Max. :1.0000
# Matriz de correlaciones
cor_data <- cor(data[, -1])
corrplot(cor_data)
#chart.Correlation(data[, -1]) # para graficar correlciones y histogramas
###
# Test de barlett
cortest.bartlett(cor_data, n= 1847)
## $chisq
## [1] 386094.2
##
## $p.value
## [1] 0
##
## $df
## [1] 2628
# Test de KMO
KMO(data[, -1])
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data[, -1])
## Overall MSA = 0.91
## MSA for each item =
## WSR0 WSR1 WSR2 WSR3 WSR4 WSR5 WSR6 WSR7 WSR8 WSR9 WSR10
## 0.80 0.86 0.87 0.88 0.88 0.86 0.88 0.79 0.79 0.84 0.82
## WSR11 WSR12 WSR13 WSR14 WSR15 WSR16 WSR17 WSR18 WSR19 WSR20 WSR21
## 0.84 0.84 0.82 0.81 0.81 0.79 0.77 0.77 0.79 0.79 0.81
## WSR22 WSR23 WSR_PK WSR_AV T0 T1 T2 T3 T4 T5 T6
## 0.85 0.72 0.98 0.70 0.94 0.96 0.95 0.96 0.95 0.95 0.95
## T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17
## 0.95 0.95 0.95 0.95 0.95 0.95 0.94 0.95 0.94 0.95 0.95
## T18 T19 T20 T21 T22 T23 T_PK T_AV T85 RH85 U85
## 0.95 0.96 0.96 0.96 0.96 0.94 0.99 0.87 0.95 0.80 0.92
## V85 HT85 T70 RH70 U70 V70 HT70 T50 RH50 U50 V50
## 0.93 0.79 0.94 0.70 0.94 0.87 0.92 0.93 0.89 0.97 0.90
## HT50 KI TT SLP SLP_ Precp clase
## 0.95 0.89 0.83 0.94 0.92 0.83 0.98
# Grafico para ver con cuantas CP nos quedamos
scree(data[, -1])
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
# Componentes Principales
cp <- prcomp(data[,-1], scale = TRUE)
#Resumen de los Componentes Principales
summary(cp)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 5.4988 3.9020 2.34036 1.78038 1.61952 1.39171 1.29335
## Proportion of Variance 0.4142 0.2086 0.07503 0.04342 0.03593 0.02653 0.02291
## Cumulative Proportion 0.4142 0.6228 0.69780 0.74123 0.77715 0.80369 0.82660
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 1.12111 1.03094 0.97706 0.93474 0.9124 0.88738 0.8195
## Proportion of Variance 0.01722 0.01456 0.01308 0.01197 0.0114 0.01079 0.0092
## Cumulative Proportion 0.84382 0.85838 0.87146 0.88342 0.8948 0.90562 0.9148
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.79731 0.74319 0.7248 0.65178 0.60062 0.56083 0.53042
## Proportion of Variance 0.00871 0.00757 0.0072 0.00582 0.00494 0.00431 0.00385
## Cumulative Proportion 0.92352 0.93109 0.9383 0.94411 0.94905 0.95336 0.95721
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 0.51635 0.48805 0.47955 0.45328 0.42993 0.41354 0.39410
## Proportion of Variance 0.00365 0.00326 0.00315 0.00281 0.00253 0.00234 0.00213
## Cumulative Proportion 0.96086 0.96413 0.96728 0.97009 0.97262 0.97497 0.97709
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 0.37897 0.35346 0.33661 0.33322 0.32211 0.30525 0.29897
## Proportion of Variance 0.00197 0.00171 0.00155 0.00152 0.00142 0.00128 0.00122
## Cumulative Proportion 0.97906 0.98077 0.98232 0.98384 0.98527 0.98654 0.98777
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 0.29204 0.2837 0.2699 0.26409 0.26011 0.24888 0.24464
## Proportion of Variance 0.00117 0.0011 0.0010 0.00096 0.00093 0.00085 0.00082
## Cumulative Proportion 0.98894 0.9900 0.9910 0.99199 0.99292 0.99377 0.99459
## PC43 PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 0.23653 0.2263 0.21912 0.2096 0.20121 0.17978 0.14170
## Proportion of Variance 0.00077 0.0007 0.00066 0.0006 0.00055 0.00044 0.00028
## Cumulative Proportion 0.99535 0.9960 0.99671 0.9973 0.99787 0.99831 0.99859
## PC50 PC51 PC52 PC53 PC54 PC55 PC56
## Standard deviation 0.12678 0.12412 0.11673 0.09661 0.09552 0.08146 0.07355
## Proportion of Variance 0.00022 0.00021 0.00019 0.00013 0.00012 0.00009 0.00007
## Cumulative Proportion 0.99881 0.99902 0.99920 0.99933 0.99946 0.99955 0.99962
## PC57 PC58 PC59 PC60 PC61 PC62 PC63
## Standard deviation 0.06466 0.06218 0.05704 0.05084 0.04829 0.04369 0.04249
## Proportion of Variance 0.00006 0.00005 0.00004 0.00004 0.00003 0.00003 0.00002
## Cumulative Proportion 0.99968 0.99973 0.99978 0.99981 0.99984 0.99987 0.99989
## PC64 PC65 PC66 PC67 PC68 PC69 PC70
## Standard deviation 0.03750 0.03411 0.03324 0.02997 0.02883 0.02751 0.02460
## Proportion of Variance 0.00002 0.00002 0.00002 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion 0.99991 0.99993 0.99995 0.99996 0.99997 0.99998 0.99999
## PC71 PC72 PC73
## Standard deviation 0.02234 0.01989 0.004221
## Proportion of Variance 0.00001 0.00001 0.000000
## Cumulative Proportion 0.99999 1.00000 1.000000
names(cp)
## [1] "sdev" "rotation" "center" "scale" "x"
cp$center # Media de cada variable
## WSR0 WSR1 WSR2 WSR3 WSR4
## 1.62902003 1.57038441 1.53697888 1.52008663 1.51526800
## WSR5 WSR6 WSR7 WSR8 WSR9
## 1.53367623 1.63243097 2.03789930 2.52577152 2.84315106
## WSR10 WSR11 WSR12 WSR13 WSR14
## 2.98159177 3.02972388 3.05668652 3.11429345 3.18500271
## WSR15 WSR16 WSR17 WSR18 WSR19
## 3.23459664 3.20184082 2.94385490 2.58175420 2.30135355
## WSR20 WSR21 WSR22 WSR23 WSR_PK
## 2.10481862 1.95343801 1.81402274 1.71754196 4.17623173
## WSR_AV T0 T1 T2 T3
## 2.31613427 18.98186248 18.66134272 18.38213319 18.13508392
## T4 T5 T6 T7 T8
## 17.92008663 17.78673525 17.92403898 18.77249594 20.14775311
## T9 T10 T11 T12 T13
## 21.58570655 22.82149432 23.73936113 24.36924743 24.78413644
## T14 T15 T16 T17 T18
## 25.03789930 25.03654575 24.71402274 23.93795344 22.80498105
## T19 T20 T21 T22 T23
## 21.73470493 20.94087710 20.35473741 19.84661613 19.39794261
## T_PK T_AV T85 RH85 U85
## 25.88787223 21.15944775 13.71396860 0.57639957 1.97608013
## V85 HT85 T70 RH70 U70
## 1.94066594 1533.69978343 6.07374120 0.39885219 5.16504061
## V70 HT70 T50 RH50 U50
## 1.01033568 3148.36410395 -10.50070384 0.30008663 9.82119112
## V50 HT50 KI TT SLP
## 0.64720087 5822.42555495 10.68040065 37.68933406 10165.47644829
## SLP_ Precp clase
## -0.83649161 0.35878722 0.06930157
cp$scale # Desviacion estandard de cada variable
## WSR0 WSR1 WSR2 WSR3 WSR4 WSR5 WSR6
## 1.2532611 1.2437166 1.2188223 1.1948800 1.1875413 1.1587215 1.1376230
## WSR7 WSR8 WSR9 WSR10 WSR11 WSR12 WSR13
## 1.1543284 1.1723598 1.2082577 1.2908659 1.3787935 1.4098352 1.4312642
## WSR14 WSR15 WSR16 WSR17 WSR18 WSR19 WSR20
## 1.4196719 1.3722366 1.2737354 1.2360134 1.2425499 1.2302099 1.2227545
## WSR21 WSR22 WSR23 WSR_PK WSR_AV T0 T1
## 1.2268828 1.2465134 1.2809022 1.1738729 0.9194124 6.7906970 6.8566936
## T2 T3 T4 T5 T6 T7 T8
## 6.9188639 6.9765000 7.0231379 7.0843190 7.2866748 7.6174112 7.5985732
## T9 T10 T11 T12 T13 T14 T15
## 7.4621944 7.3728351 7.3057891 7.2508539 7.1722525 7.0984261 7.0335815
## T16 T17 T18 T19 T20 T21 T22
## 6.9870445 6.9459143 6.8523238 6.6863457 6.6180563 6.6115504 6.6437475
## T23 T_PK T_AV T85 RH85 U85 V85
## 6.6997878 6.8585771 6.7485889 4.7600549 0.2546395 4.5163844 6.0906795
## HT85 T70 RH70 U70 V70 HT70 T50
## 35.3220501 3.7995667 0.2619168 6.3273848 6.2918610 46.6540766 3.8053869
## RH50 U50 V50 HT50 KI TT SLP
## 0.2445791 9.3428061 7.3520233 75.7110874 20.1705541 11.0074477 52.0564674
## SLP_ Precp clase
## 34.1348151 1.2625728 0.2540350
cp$sdev # Varianza de cada componente
## [1] 5.498767773 3.902047771 2.340364190 1.780378929 1.619516746 1.391706901
## [7] 1.293346613 1.121108044 1.030940921 0.977055803 0.934741210 0.912429743
## [13] 0.887376899 0.819502283 0.797312911 0.743189056 0.724785586 0.651782020
## [19] 0.600615142 0.560833724 0.530420651 0.516352374 0.488048006 0.479551788
## [25] 0.453278141 0.429932314 0.413538248 0.394103454 0.378974051 0.353456841
## [31] 0.336606710 0.333215605 0.322114566 0.305253491 0.298967201 0.292041287
## [37] 0.283660896 0.269932364 0.264089940 0.260109298 0.248875711 0.244641934
## [43] 0.236525329 0.226346963 0.219122578 0.209591385 0.201214080 0.179778346
## [49] 0.141698627 0.126783554 0.124121998 0.116729060 0.096606035 0.095521667
## [55] 0.081456190 0.073554445 0.064660029 0.062178873 0.057043689 0.050840701
## [61] 0.048291880 0.043685167 0.042487348 0.037497870 0.034107788 0.033242903
## [67] 0.029965233 0.028833463 0.027505974 0.024602185 0.022337151 0.019891745
## [73] 0.004220875
#cp$rotation
#cp$x
# para graficar:
biplot(x = cp, scale = 0, cex = 0.6, col = c("grey", "brown3"))
pc1 <- cp[[2]][,1]
pc2 <- cp[[2]][,2]
pc3 <- cp[[2]][,3]
pc4 <- cp[[2]][,4]
pc5 <- cp[[2]][,5]
pc6 <- cp[[2]][,6]
pc7 <- cp[[2]][,7]
pc <- data.frame(cbind(pc1, pc2, pc3, pc4, pc5, pc6, pc7))
rm(pc1, pc2, pc3, pc4, pc5, pc6, pc7)
fig <- plot_ly(x = ~pc[1,], y = ~pc$pc1, name = "PC1", type = "bar", text = rownames(pc), textangle=-90, textposition='auto')%>%
layout(
title = "PC1",
xaxis = list(title = "Variables", showticklabels=FALSE),
yaxis = list(title = "Valores")
)
fig
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
fig1 <- plot_ly(x = ~pc[2,], y = ~pc$pc2, name = "PC2", type = "bar", text = rownames(pc), textangle=-90, textposition='auto')%>%
layout(
title = "PC2",
xaxis = list(title = "Variables", showticklabels=FALSE),
yaxis = list(title = "Valores")
)
fig1
fig2 <- plot_ly(x = ~pc[2,], y = ~pc$pc3, name = "PC3", type = "bar", text = rownames(pc), textangle=-90, textposition='auto')%>%
layout(
title = "PC3",
xaxis = list(title = "Variables", showticklabels=FALSE),
yaxis = list(title = "Valores")
)
fig2